Point Adjustment.
Point adjustment initially introduced in [1]} is a protocol that adjusts the predictions before computing performance metrics. It acts as follows: if at least one point is classified as an anomaly in an outlier segment, all the predictions in that segment are set to anomalous. The idea behind this protocol is that an algorithm triggering an alert for any point in a contiguous anomaly segment might be sufficient for a timely reaction. Fig. REF  illustrates the point adjustment protocol by showing the ground-truth, the original predictions, and the predictions after point adjustment of a given time-series. After applying the point adjustment protocol, the F1-Score goes from \(0.32\)  to \(0.85\) . This significant gap has therefore raised some concerns in the literature regarding the use of point adjustment. For example, Kim et al. [2]} claim that by using this protocol, a randomly generated anomaly score might outperform several recently proposed time-series anomaly detection algorithms.
In this paper, we report the performance of existing methods with and without point adjustment.
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